For a plurality of medical applications, (e.g., in medical diagnostics or in medical therapy), the use of a great diversity of imaging modalities is widely known. In these applications, such diagnostics or such a therapy may be undertaken in many cases using a single medical image dataset, e.g., a single computed tomography (CT) image dataset or a magnetic resonance (MR) image dataset.
Most notably with more complex problems, but also within the framework of the monitoring of medical interventions, the use of a number of image sources, thus medical image datasets, is frequently required, for example, a CT image dataset and a Positron Emission Tomography (PET) image dataset. Such different medical image datasets represent different image information, in the example of the CT image dataset anatomical structures, in the example of the PET image dataset the metabolism and/or functional information. A significant added value compared to a separate display of these independent medical image datasets is obtained in an overlaid presentation of the medical image datasets as an overlay image. For example, the medical image datasets may be registered with one another and subsequently displayed overlaid, so that a spatial correspondence of the different sources of information is created. An overlay image thus makes it possible for a user immediately to grasp the different items of image information together.
The display of a number of items of image information from different image sources in a single overlay image leads to further challenges and even to restrictions. One problem that arises will be explained below using the example of a roadmap procedure in an angiographic intervention. In this case two independent medical image datasets of Digital Subtraction Angiography (DSA) may be displayed overlaid, namely on the one hand a first medical image dataset, which has been recorded with the application of contrast media, so that only the blood vessel tree is presented, as a second medical image dataset a current live subtraction image without application of contrast medium, which shows an instrument, (e.g., a guide wire), used within the framework of the minimally-invasive intervention.
The result of displaying the vessels in the first medical image dataset here may be extremely high intensity values of the display, e.g., by the overlaying of a number of blood vessels. There is thus a very high image value present in such regions, so that a bright display is produced. Individual vessels are not clearly shown here, so this may already result in sharp differences in contrast within the displayed blood vessel system. The second medical image dataset dependent thereon, the live subtraction image, which shows the instrument, is characterized by a constant level of contrast however, which means that the instrument, (e.g., a guide wire), is displayed consistently over its entire length. If these two medical image datasets are now overlaid, the instrument may be only recognized with great difficulty in very bright vessel regions, which may lead to a misinterpretation of the overlay image. Similar problems also arise in other application areas, for example, if one of the medical image datasets exploits the available intensity dynamics of the display to the maximum for example and thus the result may display problems at specific contrasts of the other medical image dataset.